Flood Vulnerability Analysis

1. Flood Vulnerability Analysis Session 2 Dr. HeikoApel Risk Analysis Flood Vulnerability Analysis

2. Learning objectives • Get familiar: • With the principles of flood vulnerability analysis • With the elements at risk • Learn: • The exposure mapping of elements at risk • Systematic classification of flood losses • How to collect food loss data and build flood loss models in general • Specific insights in the flood loss analysis of buildings, agriculture and lives • Understand: • The importance of impact and resilience on flood losses Risk Analysis Flood Vulnerability Analysis

3. Vulnerability analysis provides • Understanding of vulnerabilities in order to identify efficient measures to reduce them and to minimize impact of future floods • The foundation for cost-effective planning of flood mitigation • The inputs for realistic flood scenario modeling emergency planning • Data for risk mapping to be used to improve public flood risk awareness, which can motivate precautionary measures • Input for financial appraisals • for (re-)insurance todetermine insurance rates, estimate probable maximum loss (PML) • to support disaster response and governmental decisions about loss compensation immediately after floods • Data for the quantitative comparison of different risks within community or region Risk Analysis Flood Vulnerability Analysis

4. Elements at Risk • Economic sectors • Housing • Infrastructure (traffic, power supply, water supply, administration) • Food production • Transport • Trade • Cultural sectors • Cultural heritage • Daily life • Social sectors • Population • Health care • Food supply • Mobility • Environmental Sectors • Ecosystem stability • Environmental health (Pollution) • Biodiversity Risk Analysis Flood Vulnerability Analysis

5. Exposure Databases • Data should reflect the location and value of the assets at risk • Use analog maps or preferably GeoInformationSystems (GIS) for spatial reference, display and intersection • Explore the capabilities of remote sensing (RS) products for mapping of elements at risk • Most commonly used satellite imagery libraries: MODIS, LandSAT, ASTER, Quickbird, SPOT • Utilize statistical databases and spatial disaggregation methods to distribute aggregated values (e.g. dasymetric mapping) Risk Analysis Flood Vulnerability Analysis

6. Exposure Databases (cont.) Examples: Micro-scale: detailed topography and building location Meso-scale: CORINE land use data set Risk Analysis Flood Vulnerability Analysis

7. Increase of flood plains and losses • Loss increase in the last decades: • Caused by: • Increasing number of disasters • River training • Increasing use of floodplains (urbanization, population growth) • The accumulation of valuable goods • Decreasing awareness of flood risk Risk Analysis Flood Vulnerability Analysis

8. Flood impact classification Risk Analysis Flood Vulnerability Analysis

9. Factors affecting flood loss • Example: flood loss of buildings • Impact and resistance should be defined for every element at risk Source: Thieken et al. (2005) Risk Analysis Flood Vulnerability Analysis

10. The role of awareness and preparedness • Significantly reduces flood losses • Example: loss in private households in the flood of 2002 in Germany (questionnaire results, n  2150) Mean damage reduction due to precautionary measures: 29.000 € (buildings) 31.000 € (assets) 24.000 € (utilities) Source: Kreibich et al. (2005) Risk Analysis Flood Vulnerability Analysis

11. Scales of loss estimation Scale of analysis determines data and methods: • Micro-scale • Object specific • Detailed input datasets (direct surveys) required • Cities, communes, counties • Results aggregated, but detailed results available • Meso-scale • Regional to national • Aggregated input data (statistics, census data, land use units) • Cumulated loss estimates, no site specific interpretation possible Risk Analysis Flood Vulnerability Analysis

12. Flood loss data collection Is the first step for establishment and evaluation of loss models: • Problem: data availability and compatibility • Different stakeholders (e.g. insurance industry, science, public administration) collect data on flood losses with different methods: • Methods to collect data • Building Surveyors – high level of standardization, consistent data quality, limited set of parameters, expensive method (100 € per case*) • Questionnaires – answers dependent on respondents, unknown data quality, representativeness via sampling, lower cost (25-40 € per case*) * Prices refer to Germany Risk Analysis Flood Vulnerability Analysis

13. Flood loss assessment - buildings • Region specific • Damage types: • Structural damage • Contents • Differentiated into • Building types: construction, materials, size, stories • Building uses: private, commercial, industrial • Loss estimation • Absolute or relative damage • Loss functions: functional relationship between flood indicators and damage (be careful to consider all factors) • Solution: rule based loss model Risk Analysis Flood Vulnerability Analysis

14. Flood loss assessment – buildings (cont.) Step 1: damage ratio estimation by water depths and rule based model FLEMOps (step functions) • Rule-based flood Loss Estimation Model FLEMO (GFZ) Step 2: modification of loss ratio (FLEMOps+) Source: Büchele et al. (2006) Risk Analysis Flood Vulnerability Analysis

15. Flood loss assessment - agriculture Two-step process: • Relative loss estimation respective to season • Estimation of regional market value of crops Loss of wheat crops in an early summer flood in East Germany Risk Analysis Flood Vulnerability Analysis

16. Flood loss assessment – loss of life • Data-based empirical approach (based on flash flood and dam failure data in USA, LOL = f(PAR)*, Source:Brown & Graham 1988) • Process-based approaches(simulates exposure of people in buildings etc., evacuation possibilities and survival rates) *Loss of life (LOL), Population at risk (PAR) Risk Analysis Flood Risk Analysis

17. Flood loss assessment– loss of life (cont.) • Example of processed-based approach: • Hydraulic experiments by REDSCAM (2000), Helsinki University of Technology, Finland Risk Analysis Flood Risk Analysis